An oil (or gas) reservoir is an accumulation of one or more types of hydrocarbon within porous permeable sedimentary rocks, the hydrocarbons being stored within interconnected pores within those rocks. The pore volume within a reservoir is quantified by a parameter known as porosity, whereas the degree of the interconnection between the pores is referred as permeability. In an actual oil/gas reservoir, both porosity and permeability and other reservoir properties may vary from place to place. The more variation that these and other parameters exhibit spatially the more heterogeneous the reservoir will be.
Generally speaking, most reservoirs can be classified as either sandstone reservoirs or carbonate reservoirs based on their composition. Sandstone reservoirs contain elastic materials for the most part, whereas, carbonate reservoirs mostly contain limestone. Both of these types of reservoirs can be either homogeneous or heterogeneous. Homogeneous reservoirs can often be successfully produced (i.e., efficiently drained) by drilling only a few wells into them. Heterogeneous reservoirs on the other hand may need a few more (or many more) wells in order to be fully produced. FIG. 3 conceptually illustrates this point. FIG. 3A contains a simple schematic that illustrates production from a sandstone reservoir that contains two sand bodies 310 and 320 that are encased in a non-permeable rock such as shale. Since the two bodies 310 and 320 are not in fluid communication with each other, two wells will be needed in order to fully drain both bodies 310 and 320—one drilled into each. On the other hand, only a single well might be required if the two sand bodies were connected (FIG. 3B) or otherwise in fluid communication with each other. Obviously, the interconnections between subsurface reservoirs can be of substantial importance to a party who seeks to withdraw hydrocarbons from them.
Numerical reservoir models are regularly used by petroleum engineers and others to help predict the quantity of hydrocarbons that can be withdrawn from a subsurface reservoir and to help manage the process of producing it by, for example, selecting a field or well production rate, determining whether or not enhanced recovery would be cost-effective, etc. As is well known to those of ordinary skill in the art, a reservoir model is a mathematical/digital representation of an underground oil and/or gas reservoir. Generally speaking, reservoir models can be categorized as being either static or dynamic. A static model represents the reservoir without considering the fluid flow within it, whereas a dynamic model represents the reservoir and includes its fluid flow properties.
In both kinds of models, the reservoir simulation process begins by discritizing a subsurface model into a large number of grid blocks, the size of each grid block determining the resolution of the resulting reservoir model. In most cases the grid blocks will be rectangular, but other shapes are certainly possible. Of course, having more grid blocks (i.e., higher resolution) is generally better than having fewer (i.e., lower resolution) since the subsurface tends to be relatively complex. Low resolution models may omit subsurface details that could have a significant impact on production. Thus, generally speaking, the higher the resolution of the model the greater the likelihood that the model results will match those observed in the field. The size of the grid block determines the resolution of the model with small grid blocks being associated with higher resolution models.
Once a grid block system has been established, reservoir properties, such as the type of rock, its porosity and permeability, etc., are assigned to each block using methods well known to those of ordinary skill in the art. In general, such parameters can be reliably determined and assigned to grid blocks that are penetrated by a logged well, but as the distance between a grid block and the nearest well increases, the reliability of the parameters that are assigned to those blocks decreases accordingly. For purposes of the instant disclosure it will be assumed that each grid block is internally homogeneous so that the same physical parameters apply through the block. Of course, those of ordinary skill in the art will recognize that where this assumption is not valid it would certainly be possible to address that problem in any number of ways (e.g., by increasing the resolution/decreasing the size of the grid blocks until each is at least approximately homogenous internally).
A typical static reservoir model might consist of several million (e.g., seven million) grid blocks, whereas a dynamic model will typically consist of only about one million at a maximum. With increasing computer power, these numbers will change. However, the static model will always have higher resolution than the dynamic model. This is because the calculations that predict the behavior of a dynamic model are much more involved than those that would be applied to a corresponding static model. Of course, reducing the resolution of a model is a well-known method of reducing the computer power, memory, etc., required to calculate it. Computing a dynamic model of any significant size at full resolution is not economically feasible given the current state of the art in computer system speeds.
The process of converting a higher-resolution static (or other) model to a lower-resolution model for use in a dynamic model calculation is known as upscaling. This process is typically performed automatically/algorithmically when a high resolution static model is to be used in a dynamic modeling scheme. However, the conventional methods of upscaling are subject to a variety of problems which can introduce errors into a prediction that has been obtained using such models.
Thus, there is a significant difference between the amount of detail that can be accommodated by a static model and that that can be accommodated by a dynamic one. Using current technology and current computer resources, the reservoir modeler may build a static model that consists of many millions of grid blocks. This is done so that the heterogeneity of the reservoir can be replicated as closely as possible within the model. On the other hand, the complexities that are encountered when fluid flow equations are introduced limit the size of the model that modern computers can handle economically. This restriction comes about because of the need for huge amounts of computer memory and long run times if a complete a flow (dynamic) simulation is to be run. Static models that contain multiple millions of grid blocks in size may be readily calculated but, given the current limitations of computer power, a dynamic model can realistically only contain up to a million or so blocks. Thus, this limitation has forced the flow simulation engineer to sacrifice some detail of heterogeneity, and hence, accuracy, in the reservoir model.
Accordingly it should now be recognized, as was recognized by the present inventors, that there exists, and has existed for some time, a very real need for a system and method that would address and solve the above-described problems.
Before proceeding to a description of the present invention, however it should be noted and remembered that the description of the invention which follows, together with the accompanying drawings, should not be construed as limiting the invention to the examples (or preferred embodiments) shown and described. This is so because those skilled in the art to which the invention pertains will be able to devise other forms of the invention within the ambit of the appended claims.